Control system engineering refers to a domain of engineering that deals with architectures, mechanisms and algorithms for maintaining output of a specific system or process within a desired range. Automated control systems are used extensively in industry and for example can be achieved using programmable logic controllers (PLC(s)), or in the case of more complex systems using distributed control systems (DCS) or supervisory control and data acquisition systems (SCADA).
Automated control systems rely on one or more controllers communicatively coupled to one or more field devices. Field devices (e.g. sensors, valves, switches, receivers and transmitters) are located within the process environment corresponding to the control system (for example, but not limited to, an industrial plant or system) and may be configured to perform physical or process control functions to control one or more components, processes or variables under observation within the process environment. Process controllers may be located within the process environment and are configured to receive signals from field devices, make control decisions, generate control signals and communicate with field devices.
Existing control systems rely on data received from field devices within the process environment and on archival of such data. Data generated by controllers or field devices within a process environment are stored in a plurality of specifically designated databases (e.g. data historians or data silos). These plurality of databases are independent and heterogeneous, in that they may differ from one another with respect to platform, or computer system in which they reside, data model, storage, retrieval, language for implementation, interfaces and languages for querying and updating, schema, and data types. Among other reasons, the large number of heterogeneous field devices typically implemented within a control system results in data from the control system being stored in a wide variety of different data formats and at different frequencies.
Structuring databases and collecting and storing data in a heterogeneous or distributed manner presents obstacles to unified access and to interpretation of control system data. For example, root cause analysis of events within a process environment may require access to data retrieved from multiple databases. Differing data formats and collection frequencies implemented by various field devices, along with heterogeneous database file structures, database attributes and database interfaces prevents data from being accessed, analysed or interpreted in a comprehensive way, and interferes with consolidated audit, synchronization or monitoring activity within a control system.
These limitations have consequences for efficient monitoring and control of process environments—including in connection with real time system monitoring, problem detection, problem analysis, remedial action and/or predictive modelling. Overcoming such limitations requires time consuming preparatory work flows that must be performed to extract and rationalize data from multiple databases before executing a desired system related operation. Data that is obtained as a result of the preparatory work flows may suffer from inaccuracies as a consequence of misinterpretation, shifted time stamps, or problems in synchronization. Yet further, storage and retrieval of data from multiple databases may result in useful data being inadvertently discarded as irrelevant or unusable, and in useful data patterns or relationships remaining obscured.
There is therefore a need for automated and intelligent extraction, consolidation and interpretation of heterogeneous control system data that is received from a variety of field devices or other data points within a process environment or that stored across multiple databases associated with the process environment. There is additionally a need for identifying relationships between multiple data points within a process environment—to enable accurate modelling of the process environment, and to optimize system operations, problem detection, problem analysis, remedial action and/or predictive modelling based on the identified relationships.